Implementation of the Multi-Planar UNet as described in:
Mathias Perslev, Erik Dam, Akshay Pai, and Christian Igel. One Network To Segment Them All: A General, Lightweight System for Accurate 3D Medical Image Segmentation. In: Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019
Pre-print version: https://arxiv.org/abs/1911.01764
Published version: https://doi.org/10.1007/978-3-030-32245-8_4
# From GitHub
git clone https://github.com/perslev/MultiPlanarUNet
pip install -e MultiPlanarUNet
This package is still frequently updated and it is thus recommended to install the package with PIP with the -e ('editable') flag so that the package can be updated with recent changes on GitHub without re-installing:
cd MultiPlanarUNet
git pull
However, the package is also occasionally updated on PyPi for install with:
# Note: renamed MultiPlanarUNet -> mpunet in versions 0.2.4
pip install mpunet
usage: mp [script] [script args...]
Multi-Planar UNet (0.1.0)
-------------------------
Available scripts:
- cv_experiment
- cv_split
- init_project
- predict
- predict_3D
- summary
- train
- train_fusion
...
This package implements fully autonomous deep learning based segmentation of any 3D medical image. It uses a fixed hyperparameter set and a fixed model topology, eliminating the need for conducting hyperparameter tuning experiments. No manual involvement is required except for supplying the training data.
The system has been evaluated on a wide range of tasks covering various organ and pathology segmentation tasks, tissue types, and imaging modalities. The model obtained a top-5 position at the 2018 Medical Segmentation Decathlon (http://medicaldecathlon.com/) despite its simplicity and computational efficiency.
This software may be used as-is and does not require deep learning expertise to get started. It may also serve as a strong baseline method for general purpose semantic segmentation of medical images.
The base model is a slightly modified 2D UNet (https://arxiv.org/abs/1505.04597) trained under a multi-planar framework. Specifically, the 2D model is fed images sampled across multiple views onto the image volume simultaneously:
At test-time, the model predict along each of the views and recreates a set of full segmentation volumes. These volumes are fused into one using a learned function that weights each class from each view individually to maximise the performance.
Project initialization, model training, evaluation, prediction etc. can be
performed using the scripts located in MultiPlanarUNet.bin. The script
named mp.py serves as an entry point to all other scripts, and it is used
as follows:
# Invoke the help menu
mp --help
# Launch the train script
mp train [arguments passed to 'train'...]
# Invoke the help menu of a sub-script
mp train --helpYou only need to specify the training data in the format described below. Training, evaluation and prediction will be handled automatically if using the above scripts.
In order to train a model to solve a specific task, a set of manually annotated images must be stored in a folder under the following structure:
./data_folder/
|- train/
|--- images/
|------ image1.nii.gz
|------ image5.nii.gz
|--- labels/
|------ image1.nii.gz
|------ image5.nii.gz
|- val/
|--- images/
|--- labels/
|- test/
|--- images/
|--- labels/
|- aug/ <-- OPTIONAL
|--- images/
|--- labels/
The names of these folders may be customized in the parameter file (see below), but default to those shown above. The image and corresponding label map files must be identically named.
The aug folder may store additional images that can be included during
training with a lower weight assigned in optimization.
All images must be stored in the .nii/.nii.gz format.
It is important that the .nii files store correct 4x4 affines for mapping
voxel coordinates to the scanner space. Specifically, the framework needs to
know the voxel size and axis orientations in order to sample isotrophic images
in the scanner space.
Images should be arrays of dimension 4 with the first 3 corresponding to the image dimensions and the last the channels dimension (e.g. [256, 256, 256, 3] for a 256x256x256 image with 3 channels). Label maps should be identically shaped in the first 3 dimensions and have a single channel (e.g. [256, 256, 256, 1]). The label at a given voxel should be an integer representing the class at the given position. The background class is normally denoted '0'.
Once the data is stored under the above folder structure, a Multi-Planar project can be initialized as follows:
# Initialize a project at 'my_folder'
# The --data_dir flag is optional
mp init_project --name my_project --data_dir ./data_folder
This will create a folder at path my_project and populate it with a YAML
file named train_hparams.yaml, which stores all hyperparameters. Any
parameter in this file may be specified manually, but can all be set
automatically.
NOTE: By default the init_project prepares a Multi-Planar model.
However, note that a 3D model is also supported, which can be selected by
specifying the --model=3D flag (default=---model=MultiPlanar).
The model can now be trained as follows:
mp train --num_GPUs=2 # Any number of GPUs (or 0)
During training various information and images will be logged automatically to the project folder. Typically, after training, the folder will look as follows:
./my_project/
|- images/ # Example segmentations through training
|- logs/ # Various log files
|- model/ # Stores the best model parameters
|- tensorboard/ # TensorBoard graph and metric visualization
|- train_hparams.yaml # The hyperparameters file
|- views.npz # An array of the view vectors used
|- views.png # Visualization of the views used
When using the MultiPlanar model, a fusion model must be computed after the base model has been trained. This model will learn to map the multiple predictions of the base model through each view to one, stronger segmentation volume:
mp train_fusion --num_GPUs=2
The trained model can now be evaluated on the testing data in
data_folder/test by invoking:
mp predict --num_GPUs=2 --out_dir predictions
This will create a folder my_project/predictions storing the predicted
images along with dice coefficient performance metrics.
The model can also be used to predict on images stored in the predictions
folder but without corresponding label files using the --no_eval flag or on
single files as follows:
# Predict on all images in 'test' folder without label files
mp predict --no_eval
# Predict on a single image
mp predict -f ./new_image.nii.gz
# Preidct on a single image and do eval against its label file
mp predict -f ./im/new_image.nii.gz -l ./lab/new_image.nii.gz
A summary of the performance can be produced by invoking the following command
from inside the my_project folder or predictions sub-folder:
mp summary
>> [***] SUMMARY REPORT FOR FOLDER [***]
>> ./my_project/predictions/csv/
>>
>>
>> Per class:
>> --------------------------------
>> Mean dice by class +/- STD min max N
>> 1 0.856 0.060 0.672 0.912 34
>> 2 0.891 0.029 0.827 0.934 34
>> 3 0.888 0.027 0.829 0.930 34
>> 4 0.802 0.164 0.261 0.943 34
>> 5 0.819 0.075 0.552 0.926 34
>> 6 0.863 0.047 0.663 0.917 34
>>
>> Overall mean: 0.853 +- 0.088
>> --------------------------------
>>
>> By views:
>> --------------------------------
>> [0.8477811 0.50449719 0.16355361] 0.825
>> [ 0.70659414 -0.35532932 0.6119361 ] 0.819
>> [ 0.11799461 -0.07137918 0.9904455 ] 0.772
>> [ 0.95572575 -0.28795306 0.06059151] 0.827
>> [-0.16704373 -0.96459936 0.20406974] 0.810
>> [-0.72188903 0.68418977 0.10373322] 0.819
>> --------------------------------
Cross validation experiments may be easily performed. First, invoke the
mp cv_split command to split your data_folder into a number of
random splits:
mp cv_split --data_dir ./data_folder --CV=5
Here, we prepare for a 5-CV setup. By default, the above command will create a
folder at data_folder/views/5-CV/ storing in this case 5 folders
split0, split1, ..., split5 each structured like the main data folder
with sub-folders train, val, test and aug (optionally,
set with the --aug_sub_dir flag). Inside these sub-folders, images a
symlinked to their original position to safe storage.
A cross-validation experiment can now be performed. On systems with multiple GPUs, each fold can be assigned a given number of the total pool of GPUs'. In this case, multiple folds will run in parallel and new ones automatically start when previous folds terminate.
First, we create a new project folder. This time, we do not specify a data folder yet:
mp init_project --name CV_experiment
We also create a file named script, giving the following folder structure:
./CV_experiment
|- train_hparams.yaml
|- script
The train_hparams.yaml file will serve as a template that will be applied
to all folds. We can set any parameters we want here, or let the framework
decide on proper parameters for each fold automatically. The script file
details the mp commands (and optionally various arguments) to execute on
each fold. For instance, a script file may look like:
mp train --no_images # Do not save example segmentations
mp train_fusion
mp predict --out_dir predictions
We can now execute the 5-CV experiment by running:
mp cv_experiment --CV_dir=./data_dir/views/5-CV \
--out_dir=./splits \
--num_GPUs=2
--monitor_GPUs_every=600
Above, we assign 2 GPUs to each fold. On a system of 8 GPUs, 4 folds will be
run in parallel. We set --monitor_GPUs_every=600 to scan the system for
new free GPU resources every 600 seconds (otherwise, only GPUs that we
initially available will be cycled and new free ones will be ignored).
The cv_experiment script will create a new project folder for each split
located at --out_dir (CV_experiment/splits in this case). For each
fold, each of the commands outlined in the script file will be launched
one by one inside the respective project folder of the fold, so that the
predictions are stored in CV_experiment/splits/split0/predictions for
fold 0 etc.
Afterwards, we may get a CV summary by invoking:
mp summary
... from inside the CV_experiment/splits folder.